Symbolic Patterns Research Articles

СИМВОЛИЧЕСКИЕ ПАТТЕРНЫ СУБЪЕКТИВНОЙ ОЦЕНКИ БУДУЩЕГО СОВРЕМЕННОЙ МОЛОДЕЖЬЮ РОССИИ

Актуальность темы междисциплинарного осмысления символических паттернов будущего молодежью России определяется потребностью в самовыражении собственного восприятия будущего у молодого поколения через социальные коммуникации и отсутствием социально-философских работ, посвященных анализу взаимодействий между элементами познавательных аспектов и представлений о будущем в конкретных символах. Цель работы: определить символические реалии современной российской молодежи, репрезентативным свойством отражения которых на данный момент являются молодежные СМИ. Методы анализа: контент-анализ (качественный и количественный) молодежных СМИ развлекательного характера вместе с методологией социального конструктивизма для установления релятивных символических паттернов образов будущего в представлении молодого поколения. Результаты. Было установлено, что процесс конструирования концепта «образ будущего» происходит на субъективном уровне, а символ как фрагмент мышления находит отражение в современных молодежных СМИ, в рамках которых процессы знаковой динамики рефлексируют и задают правила конструирования действительности. Также исследование позволило описать триггерные точки (оптимистическая оценка личного и общего будущего России) в субъективном символизме, где главной конструирующей силой является непосредственный субъект (интервьюируемый студент) в контексте различных компонентов. При анализе молодежных изданий было выявлено, что взаимодействие субъективного представления молодежи об образах будущего, коррелирующего с усиливающейся ролью социальных медиа, отражает положительную динамику представлений относительно будущего России в студенческом сообществе, но при этом показывает низкую вовлеченность молодого поколения страны в коллективные цели, в выстраивание идеи общего российского будущего в паттернах положительной направленности. Отмечается, что маркер «Карьера» отображается в качестве основной триггерной точки, которая является ключевым показателем собственного будущего для молодого поколения.

From everyday practice to structural injustice

The “#metoo movement” has led to an increased awareness of the kind of structural injustice that makes it hard to identify, and get rid of, sexual harassment and gender discrimination. Worker representatives, union coordinators and politicians have stated that they will pay more attention to how structural patterns of interpretation, symbols, stereotypes, and norms, are contributing to silencing the voices of the victims. My paper is not about #metoo movement or sexual harassment per se, but about the kinds of injustice embedded in ways of communicating and in how we assess each other’s’ statements and behavior. The first part of my paper discusses how epistemic injustice, implicit bias and micro injustice operates within our everyday social practices. The aim is to show how these forms of injustice are contributing to support collective frames of interpretation that need to change in order to realize gender equality. In the second part of the paper, I argue that the best solution to avoid the negative effects of implicit bias, micro inequality and epistemic injustice, is to make institutions responsible Social, political and legal institutions should be organized in a way that reduces negative effects of structural injustice. Inspired by Elisabeth Andersson’s critique of Miranda Frickers concept of epistemic virtue, I discuss three reasons why this should be a case for institutions. First, all people tend to assess other people according to implicit biases. It is not a question of people’s bad character, but a question of cultural narratives. Second, structural injustice is often rooted in small micro inequalities that are not in themselves unjust. The injustice occurs when these small actions aggregate into structural patterns of inequality. Third, institutional justice aims at creating just background conditions for individual actions.

Very Large Scale Integrated-Circuit Architecture for Offset Quadrature Phase Shift Keying Modulation Using Offset Generator

This work presents an architecture for Offset Quadrature Phase Shift Keying (OQPSK) using an Offset generator. The Offset generator generates symbol patterns such that there is a maximum phase shift of ±90° only between the symbols, unlike the Quadrature Phase Shift Keying (QPSK) modulator which has a maximum of ±180° phase shift. Here the effort of designing an Offset generator, which can be embedded onto QPSK modulator to generate OQPSK with minimum combinational logic, has been discussed. Whenever the input symbols to the Offset generator varies in phase with a ±180° in constellation, it generates an intermediate symbol which differs only in ±90° with the previous one. Then it continues to give out the original symbol to be transmitted. This follows the exact OQPSK technology while it doesn't follow the conventional generation method of OQPSK modulation which provides an offset between the even and odd bit patterns of the symbols to be transmitted. The architecture is modelled using Verilog HDL and the functionality is validated from the simulation result.

On time series classification with dictionary-based classifiers

A family of algorithms for time series classification (TSC) involve running a sliding window across each series, discretising the window to form a word, forming a histogram of word counts over the dictionary, then constructing a classifier on the histograms. A recent evaluation of two of this type of algorithm, Bag of Patterns (BOP) and Bag of Symbolic Fourier Approximation Symbols (BOSS) found a significant difference in accuracy between these seemingly similar algorithms. We investigate this phenomenon by deconstructing the classifiers and measuring the relative importance of the four key components between BOP and BOSS. We find that whilst ensembling is a key component for both algorithms, the effect of the other components is mixed and more complex. We conclude that BOSS represents the state of the art for dictionary-based TSC. Both BOP and BOSS can be classed as bag of words approaches. These are particularly popular in Computer Vision for tasks such as image classification. We adapt three techniques used in Computer Vision for TSC: Scale Invariant Feature Transform; Spatial Pyramids; and Histogram Intersection. We find that using Spatial Pyramids in conjunction with BOSS (SP) produces a significantly more accurate classifier. SP is significantly more accurate than standard benchmarks and the original BOSS algorithm. It is not significantly worse than the best shapelet-based or deep learning approaches, and is only outperformed by an ensemble that includes BOSS as a constituent module.

Color sound symbolism in natural languages

abstractThis paper investigates the underlying cognitive processes of sound–color associations by connecting perceptual evidence from research on cross-modal correspondences to sound symbolic patterns in the words for colors in natural languages. Building upon earlier perceptual experiments, we hypothesized that sonorous and bright phonemes would be over-represented in the words for bright and saturated colors. This hypothesis was tested on eleven color words and related concepts (red–green, yellow–blue, black–white, gray, night–day, dark–light) from 245 language families. Textual data was transcribed into the International Phonetic Alphabet (IPA), and each phoneme was described acoustically using high-quality IPA recordings. These acoustic measurements were then correlated with the luminance and saturation of each color obtained from cross-linguistic color-naming data in the World Color Survey. As expected, vowels with high brightness and sonority ratings were over-represented in the words for colors with high luminance, while sonorous consonants were more common in the words for saturated colors. We discuss these results in relation to lexicalization patterns and the links between iconicity and perceptual cross-modal associations.

A Parallel Computation Algorithm for Image Feature Extraction

We present a new method for image feature-extraction for learning image classification. An image is represented by a feature vector of distances that measure the dissimilarity between regions of the image and a set of fixed image prototypes. The method uses a text-based representation of images where the texture of an image corresponds to patterns of symbols in the text string. The distance between two images is based on the LZ-complexity of their corresponding strings. Given a set of input images, the algorithm produces cases that can be used by any supervised or unsupervised learning algorithm to learn image classification or clustering. A main advantage in this approach is the lack of need for any image processing or image analysis. A non-expert user can define the image-features by selecting a few small images that serve as prototypes for each class category. The algorithm is designed to run on a parallel processing platform. Results on the classification accuracy and processing speed are reported for several image classification problems including aerial imaging.

Neuronal coupling benefits the encoding of weak periodic signals in symbolic spike patterns

The biophysical mechanisms by which an input signal elicits a neuronal response are well known (sufficiently large inputs change the membrane potential of the neuron and generate electrical pulses, known as action potentials or spikes), yet, a good understanding of how neurons use these spikes to encode the signal information remains elusive. Recent theoretical studies have focused on how neurons encode a weak periodic signal (that by itself is unable to generate spikes) in a noisy environment, where stochastic electrical fluctuations that do not encode any information occur. Analyzing spike sequences generated by individual neurons and by two coupled neurons (that were simulated with the stochastic FitzHugh–Nagumo model), it has been found that the relative timing of the spikes can encode the signal information. Using a symbolic method to analyze the spike sequence, preferred and infrequent spike patterns were detected, whose probabilities vary with both, the amplitude and the frequency of the signal. To investigate if this encoding mechanism is plausible also for neuronal ensembles, here we analyze the activity of a group of neurons, when they all perceive a weak periodic signal. We find that, as in the case of one or two coupled neurons, the probabilities of the spike patterns, now computed from the spike sequences of all the neurons, depend on the signal’s amplitude and period, and thus, the patterns’ probabilities encode the information of the signal. We also find that the resonances with the period of the signal or with the noise level are more pronounced when a group of neurons perceive the signal, in comparison with when only one or two coupled neurons perceive it. Neuronal coupling is beneficial for signal encoding as a group of neurons is able to encode a small-amplitude signal, which could not be encoded when it is perceived by just one or two coupled neurons. Interestingly, we find that for a group of neurons, just a few connections with one another can significantly improve the encoding of small-amplitude signals. Our findings indicate that information encoding in preferred and infrequent spike patterns is a plausible mechanism that can be employed by neuronal populations to encode weak periodic inputs, exploiting the presence of neural noise.

The Levenshtein distance as a measure of mirror symmetry and homogeneity for binary digital patterns

Abstract. The complexity of a digital pattern, image, map, or sequence of symbols is a salient feature that finds numerous applications in a variety of domains of knowledge [1], [7], [10], [11]. Two features of patterns that form inherent components of pattern complexity, are mirror (reflection) symmetry and homogeneity [8], [9]. In the raster graphics representation mode, a pattern consists of a two-dimensional array (matrix) of elements (pixels, symbols). It is assumed here that the elements are binary-valued (black-white). With such a representation it is common to compute properties of 2-dimensional patterns, such as complexity, mirror-symmetry, and homogeneity, along the 1-dimensional rows, columns, and diagonals of the array [4]. In addition, within each row, mirror symmetries may be analysed either globally or locally [3]. A pattern that does not exhibit global mirror symmetry may still possess an abundant number of local mirror symmetries. Local symmetries permit graded measures of symmetry rather than all-or-nothing decisions. One powerful type of local symmetry is the sub-symmetry, a contiguous subset of elements of the pattern that is palindromic (has mirror symmetry). It has been shown empirically that the total number of sub-symmetries present in a pattern may serve as an excellent predictor of the perception of both visual pattern complexity [5], and auditory pattern complexity [6]. The present research project explores how two well-known measures of the distance between binary patterns and their inversions, correlate with sub-symmetries, as well as other measures of symmetry and homogeneity.

“His cramped and claustrophobic brain”: Confinement and Freedom in John Wray’s Lowboy

This article shows that ambivalence is a major symbolic pattern in John Wray’s 2009 neuronovel Lowboy, and it affects the major aspects of the novel, including the characters’ identities and the narrative structure of the book. Ambivalence may refer either to the split personality of the schizophrenic hero (psychoanalytic approach) or to the structure of the brain (neurologic approach). Especially, ambivalence allows subversion to prevail and transform the main protagonist’s mental confinement into textual freedom. This is shown, first, by an analysis of the referential framework and its major references, space and time. Then the major genres Lowboy borrows from are analyzed: on the one hand, the neuronovel suggests a bildungsroman, on the other, a mystery novel. While Wray borrows conventions from both, he also revisits and revises them, thus asserting the specificity of his novel and his freedom from generic conventions. Eventually, the manipulation of representation will be examined through an analysis of the language of description, particularly the use of incongruous images in the sections where Lowboy is the focal character.

Assessment of the Coupling Strength of Cardiovascular Control via Joint Symbolic Analysis during Postural Challenge in Recreational Athletes.

Short-term cardiovascular control, comprising cardiac baroreflex and mechanisms governing cardiac contractility and vascular properties, links heart period (HP) and systolic arterial pressure (SAP) fluctuations. It is activated during postural challenge and this activation is traditionally quantified via linear tools such as HP-SAP squared coherence function. In this study the ability of a nonlinear bivariate tool based on joint symbolic analysis (JSA) approach was tested against HP-SAP coherence function during orthostatic challenge in recreational athletes. We studied 9 men healthy cycling practitioners (age: 20-40 yrs) at rest in supine condition (REST) and during active standing (STAND). The JSA method is based on the definition of symbolic HP and SAP patterns and on the evaluation of the rate of their simultaneous occurrence in both HP and SAP series. HP-SAP squared coherence was computed in the low frequency band (LF, from 0.04 to 0.15 Hz). We found the expected response to the postural stimulus, namely the increase of sympathetic modulation and the contemporaneous vagal withdrawal. However, only JSA was able to detect the expected increase of association between HP and SAP variability series over slow time scales. This result suggests that recreational athletes have more relevant nonlinear interactions between HP and SAP that might be missed by traditional linear tools and might require nonlinear tools to be efficiently described.

Towards a semiotic theory of the corporation

ABSTRACT The firm is the central institution of modern capitalism, predominantly in the shape of the “corporation” in which entrepreneurs and investors cooperate under certain legal forms, especially limited liability. Although in modern capitalism, the emergence of these legal and organizational forms was intimately tied to technological innovation and the ongoing deployment of technologies, most economic theories about the corporation tend to neglect its material dimension, mediating between user practices, technologies and production via the mobilization of symbolic media, such as industrial design. We advance a semiotic theory of the corporation, which we relate to recent developments in sociology, especially organizational ecology. In this view, corporations embody technologies in semiosis, which is a complex multi-level process combining different dimensions that relate to various audiences, such as users or the financial community. In this process, products are embedded in diverse functional and symbolic hierarchies, defining industries and patterns of usages. We illustrate our argument in sketching the case of Tesla corporation and its potential in disrupting the automotive industry.

Hyperdimensional Computing With Local Binary Patterns: One-Shot Learning of Seizure Onset and Identification of Ictogenic Brain Regions Using Short-Time iEEG Recordings.

We develop a fast learning algorithm combining symbolic dynamics and brain-inspired hyperdimensional computing for both seizure onset detection and identification of ictogenic (seizure generating) brain regions from intracranial electroencephalography (iEEG). Our algorithm first transforms iEEG time series from each electrode into symbolic local binary pattern codes, from which a holographic distributed representation of the brain state of interest is constructed across all the electrodes and over time in a hyperdimensional space. The representation is used to quickly learn from few seizures, detect their onset, and identify the spatial brain regions that generated them. We assess our algorithm on our dataset that contains 99 short-time iEEG recordings from 16 drug-resistant epilepsy patients being implanted with 36-100 electrodes. For the majority of the patients (ten out of 16), our algorithm quickly learns from one or two seizures and perfectly (100%) generalizes on novel seizures using k-fold cross-validation. For the remaining six patients, the algorithm requires three to six seizures for learning. Our algorithm surpasses the state-of-the-art including deep learning algorithms by achieving higher specificity (94.84% versus 94.77%) and macroaveraging accuracy (95.42% versus 94.96%), and 74× lower memory footprint, but slightly higher average latency in detection (15.9s versus 14.7s). Moreover, the algorithm can reliably identify (with a p-value ) the relevant electrodes covering an ictogenic brain region at two levels of granularity: cerebral hemispheres and lobes. Our algorithm provides: 1) a unified method for both learning and classification tasks with end-to-end binary operations; 2) one-shot learning from seizure examples; 3) linear computational scalability for increasing number of electrodes; and 4) generation of transparent codes that enables post-translational support for clinical decision making. Our source code and anonymized iEEG dataset are freely available at http://ieeg-swez.ethz.ch.

Expressing evolution in Pokémon names: Experimental explorations

AbstractThere has been a growing interest in sound symbolic patterns in natural languages, in which some sounds are associated with particular meanings. Previous corpus-based research identified some specific sound symbolic relationships in Pokémon naming patterns in Japanese (Kawahara et al. 2018b). One of the main findings was that the names of Pokémon characters are more likely to contain voiced obstruents, and are longer in terms of mora count, when the Pokémon characters undergo evolution (e.g.nyoromo→nyorozo; poppo→pijotto). The current study reports three experiments that test whether (i) these patterns are productive in the minds of general Japanese speakers, and whether (ii) the same tendency holds with English speakers. The results show that the effect of phonological length was clearly observed both with Japanese and English speakers; the effects of voiced obstruents were observed clearly with Japanese speakers, but less clearly with English speakers. Along the way, we address other general issues related to sound symbolism: (iii) to what extent the sound symbolic effects identified in Kawahara et al. (2018b) rely on familiarity with Pokémon, and (iv) whether word-initial segments invoke stronger images than word-internal segments. In addition to its research value, we emphasize that this general project on Pokémon names can be useful for undergraduate phonetics education.

The Minimalist Program and the Origin of Language: A View From Paleoanthropology.

In arguing that articulate language is underpinned by an algorithmically simple neural operation, the Minimalist Program (MP) retrodicts that language emerged in a short-term event. Because spoken language leaves no physical traces, its ancient use must be inferred from archeological proxies. These strongly suggest that modern symbolic human behavior patterns – and, by extension, cognition – emerged both abruptly and late in time (subsequent to the appearance of Homo sapiens as an anatomical entity some 200 thousand years kyr ago). Because the evidence is compelling that language is an integral component of modern symbolic thought, the archeological evidence clearly supports the basic tenet of the MP. But the associated proposition, that language was externalized in an independent event that followed its initial appearance as a conduit to internal thought, is much more debatable.

Year Five Pupils’ Number Sense and Algebraic Thinking: the Mediating Role of Symbol and Pattern Sense

This study mainly focused on the relationship between number sense and algebraic thinking. Previous studies have provided evidence that number sense plays an important role in developing algebraic thinking. The role of symbol and pattern sense are yet to discover in relation to number sense and algebraic thinking. The purpose of this study was to identify the mediating effects of symbol sense and pattern sense in year five pupils’ relationship between number sense and algebraic thinking. To do so, two mathematics tests were carried out among 720 year five pupils in the district of Malacca, Malaysia. The collected data were analysed using a partial least squares-struc-tural equation modeling approach. The data collected were analysed using SPSS 22.0 and SmartPLS 3.0. Results demonstrated that symbol sense and pattern sense are good mediators between year five pupils’ number sense and algebraic thinking. This result of this study supports the past studies related to the role of number sense, symbol and pattern sense in developing algebraic thinking. The presented study provides suggestions as intervention to increase students’ making sense ability in numbers, symbols and patterns to develop algebraic thinking. © 2019, Adam Marszalek Publishing House. All rights reserved.

Abstract P357: Subclinical Hypothyroidism Alters Autonomous Nervous System. The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil)

Introduction: Lifetime stressful conditions have been related to a higher risk of coronary heart diseases events. One recognized way is related to the disbalance of a parasympathetic and sympathetic system that can be mediated by several humoral factors, including thyroid hormones. Although the role of thyroid hormones on cardiovascular system regulation on the autonomous nervous system was very well-defined decades ago for normal conditions and there is scarce information concern to the association between subclinical thyroid disorders and the autonomous nervous system. Hypothesis: Subclinical thyroid dysfunctions are associated with autonomic disturbances assessed by the heart rate variability evaluated by linear and nonlinear variables following postural changes. Methods: At baseline of the ELSA-Brasil study (15105 adults aged 35-74 years old), we evaluated the heart variability rate at the supine position for everyone, and sampled 855 participants to repeat a standing measure of the heart rate variability. Subclinical hypothyroidism was defined as thyroid-stimulating hormone >4 IU/ml for people with normal free-thyroxine levels. The cardiac autonomic nervous function was evaluated by linear time and frequency domain analyses (standard deviation of consecutive RRi - SDNN; root mean square of successive differences between adjacent regular R-R intervals - RMSSD; low frequency - LF; high frequency - HF; LF/HF ratio) as well as by nonlinear symbolic dynamics (patterns with no variation - “0V”; patterns with one variation - “1V”; patterns with two variations - 2V). Results: After exclusions, 666 (93.8%) euthyroid and 44 (6.2%) participants with subclinical hypothyroidism were eligible for analyses. At the baseline, supine rest measurement, the “0V” symbolic pattern, which reflects sympathetic modulation, was higher (31.8 vs. 24.2; P= .01) and “2V”, representing parasympathetic activity, was lower than in the euthyroid group (18.0 vs. 25.3; P= .02). Comparing the variation between positions, the “0V” pattern showed a lower delta in subclinical hypothyroidism than in euthyroid subjects (6.8 vs. 14.2%; P= .04). Additionally, it is suggested that a symbolic analysis of heart rate dynamics is an alternative and, possibly, a more sensitive method for cardiac autonomic assessment following standing position in this population. Conclusion: Subclinical hypothyroidism presented lower sympathetic and parasympathetic tonus at rest and a blunted sympathetic response to active postural change, marked by reduced variation in the “0V” of symbolic analysis.

LEMBAR KERJA DIRECTED ACTIVITIES RELATED TO TEXTS (DARTS) BERMUATAN MULTIPEL LEVEL REPRESENTASI UNTUK MENINGKATKAN KEMAMPUAN BERPIKIR KRITIS CALON GURU KIMIA

Understanding the chemistry concept should be demonstrated by the ability to transfer and connect levels of chemical representation. This study aimed to produce DARTs-worksheets containing multiple levels of representation as learning media to improve critical thinking ability. The research used the RD activity observations; as well as interviews). The subject involved 49 pre-service chemistry teachers at two universities. The results showed that (1) it was produced four worksheets with characteristics that could be viewed from critical thinking skills, DARTs activities, and multiple levels of representation; (2) the DARTs worksheet got a positive rating, the level of and very good for didactic, construction, and technical requirements, and showed the level of readability at the level; (3) worksheets directed activities that indicate an increase in critical thinking ability. Important implications of this study indicated the need to uncover the symbolic learning patterns of chemistry by chemistry teachers in high school, especially compound nomenclature material, chemical reaction equations, and interpretation of graphs, tables, and curves.

Stock Price Pattern Prediction Based on Complex Network and Machine Learning

Complex networks in stock market and stock price volatility pattern prediction are the important issues in stock price research. Previous studies have used historical information regarding a single stock to predict the future trend of the stock’s price, seldom considering comovement among stocks in the same market. In this study, in order to extract the information about relation stocks for prediction, we try to combine the complex network method with machine learning to predict stock price patterns. Firstly, we propose a new pattern network construction method for multivariate stock time series. The price volatility combination patterns of the Standard & Poor’s 500 Index (S&P 500), the NASDAQ Composite Index (NASDAQ), and the Dow Jones Industrial Average (DJIA) are transformed into directed weighted networks. It is found that network topology characteristics, such as average degree centrality, average strength, average shortest path length, and closeness centrality, can identify periods of sharp fluctuations in the stock market. Next, the topology characteristic variables for each combination symbolic pattern are used as the input variables for K‐nearest neighbors (KNN) and support vector machine (SVM) algorithms to predict the next‐day volatility patterns of a single stock. The results show that the optimal models corresponding to the two algorithms can be found through cross‐validation and search methods, respectively. The prediction accuracy rates for the three indexes in relation to the testing data set are greater than 70%. In general, the prediction ability of SVM algorithms is better than that of KNN algorithms.

Accounting for the stochastic nature of sound symbolism using Maximum Entropy model

AbstractSound symbolism refers to stochastic and systematic associations between sounds and meanings. Sound symbolism has not received much serious attention in the generative phonology literature, perhaps because most if not all sound symbolic patterns are probabilistic. Building on the recent proposal to analyze sound symbolic patterns within a formal phonological framework (Alderete and Kochetov 2017), this paper shows that MaxEnt grammars allow us to model stochastic sound symbolic patterns in a very natural way. The analyses presented in the paper show that sound symbolic relationships can be modeled in the same way that we model phonological patterns. We suggest that there is nothing fundamental that prohibits formal phonologists from analyzing sound symbolic patterns, and that studying sound symbolism using a formal framework may open up a new, interesting research domain. The current study also reports two hitherto unnoticed cases of sound symbolism, thereby expanding the empirical scope of sound symbolic patterns in natural languages.

Detecting Determinism in Time Series with Complex Networks Constructed Using a Compression Algorithm

The properties of complex networks derived from applying a compression algorithm to time series subject to symbolic ordinal-based encoding is explored. The information content of compression codewords can be used to detect forbidden symbolic patterns indicative of nonlinear determinism. The connectivity structure of ordinal-based compression networks summarized by their minimal cycle basis structure can also be used in tests for nonlinear determinism, in particular, detection of time irreversibility in a signal.